The Economic Value of Meta-Report Cards: The Case of Automobiles

Meta-report cards are product report cards that aggregate information from multiple public sources with the goal of easing consumer decision making. It is not apparent whether and how meta-report cards influence consumer decisions and market demand.

On one hand, because meta-report cards do not introduce proprietary information or sell products and simply synthesize existing information, one could question their potential to influence consumer decisions. On the other hand, synthesis of information could potentially aid in consumer search and product ratings could be a signal of quality offering.

To better understand the economic value of meta-report cards, Guneet Kaur Nagpal and Rajdeep Grewal use a revealed preferences approach with data from the U.S. automobile industry.

In 2007, U.S. News & World Report (USNWR) introduced a meta-report card (www.cars.usnews.com) that synthesized information from multiple public sources, including J.D. Power and Kelly Blue Book, among others. This offered a natural experiment, with a pre-post (before 2007 and after 2006), treatment-control (brands rated and brands not rated) design. Complementing the USNWR ratings with data from multiple other sources, the authors estimate a nested logit demand model for brand choice with aggregate data and include the USNWR rating as an endogenous product characteristic.

They show that meta-report cards offer economic value for consumers and marketers through the mechanisms of search cost reduction and quality assurance.

The presence of brands on USNWR meta-report card translates to societal benefit of $10.53 for an average consumer (with this value ranging from $2.90 to $16.89 between 2007 and 2012).

On average, one standard deviation improvement on USNWR ratings (measured on a 10-point scale with standard deviation of .58) enables a brand to charge $3560 more or save around $12 million on advertising.

Guneet Kaur Nagpal is a doctoral candidate in Marketing and Rajdeep Grewal is the Townsend Family Distinguished Professor of Marketing, both at Kenan-Flagler Business School, University of North Carolina.

Acknowledgments
The authors acknowledge research funding from the Marketing Science Institute to purchase one dataset used in the research. The authors also thank the faculty members and other attendees for their valuable feedback at research seminars at the Marketing Science Conferences 2016 - 2017, University of Cambridge, University of Notre Dame, Texas A&M University, Boston University, Duke University, and Dartmouth University. They thank James A. Dearden, Lehigh University, Adithya Pattabhiramaiah, Georgia Tech University, Huanhuan Shi, University of Nebraska-Lincoln, Srihari Sridhar, Texas A&M, and Sriram Venkatraman, UNC Chapel Hill, for their valuable feedback on the paper